Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation

Abstract

One of the primary obstacles in the advancement of Natural Language Processing (NLP) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP. The tool is Unicode-compatible, language-agnostic, Web-deployable and supports distributed annotation by multiple simultaneous annotators. The system sports user-friendly interfaces for 8 categories of annotation tasks. These, in turn, enable the annotation of a considerably larger set of NLP tasks. The task categories include two linguistic tasks not handled by any other tool, namely, sentence boundary detection and deciding canonical word order, which are important tasks for text that is in the form of poetry. We propose the idea of sequential annotation based on small text units, where an annotator performs several tasks related to a single text unit before proceeding to the next unit. The research applications of the proposed mode of multi-task annotation are also discussed. Antarlekhaka outperforms other annotation tools in objective evaluation. It has been also used for two real-life annotation tasks on two different languages, namely, Sanskrit and Bengali. The tool is available at https://github.com/Antarlekhaka/code.

Hrishikesh Terdalkar

Postdoctoral Researcher

My research lies in the intersection of Computational Linguistics, Natural Language Processing, and Graph Databases with a particular emphasis on low-resource languages such as Sanskrit and other Indian languages. I am committed to pioneering NLP innovations that have a real-world impact. I enjoy building user-friendly GUIs and CLIs for various applications. My interests also include Artificial Intelligence, Databases, Human-Computer Interaction, Information Retrieval, and Data Mining.